Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
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eduardolat
/ countries.json
Created
December 24, 2023 04:02
JSON de países, con nombre en español, ingles, código ISO y prefijo telefónico del país
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| [ | |
| { | |
| "nameES": "Afganistán", | |
| "nameEN": "Afghanistan", | |
| "iso2": "AF", | |
| "iso3": "AFG", | |
| "phoneCode": "93" | |
| }, | |
| { | |
| "nameES": "Albania", |
Note
More information you can find on Microsoft documentation.
Tip
You can install all Visual C++ Redistributable Packages using only one command with winget! https://gist.github.com/ChuckMichael/d4221fd8681a7e962c8d37d623ff3145
- x64 8.0.61000 (EOL)
Microsoft.VCRedist.2005.x64
Scripts to fully remove Claude Desktop and all the files it leaves behind, because Anthropic's official uninstaller doesn't.
Anthropic shipped a Cowork feature in Claude Desktop that silently spins up a Linux VM on your machine. No warning. No disk space prompt. No way to opt out at install time.
The VM bundle grows to ~10GB and lives in your AppData/Application Support folder. When you uninstall Claude Desktop through the normal method, the VM bundle stays behind. Most users will never find it because it's buried in a hidden system folder.
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| #!/usr/bin/env python3 | |
| # This program prints all hcn (highly composite numbers) <= MAXN (=10**18) | |
| # | |
| # The value of MAXN can be changed arbitrarily. When MAXN = 10**100, the | |
| # program needs less than one second to generate the list of hcn. | |
| from math import log | |
| MAXN = 10**18 |
fakezeta
/ qwen3.6_merged_template.jinja
Last active
June 6, 2026 17:07
Merged Qwen Multimodal Chat Template from allanchan339 and froggeric
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| {# ========================= | |
| Merged Qwen Multimodal Chat Template from | |
| - https://github.com/allanchan339/vLLM-Qwen3-3.5-3.6-chat-template-fix | |
| - https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates | |
| Features: | |
| - developer role supported (from froggeric) | |
| - <|think_on|> / <|think_off|> toggles (from froggeric) | |
| - Historical reasoning HIDDEN by default (from allanchan339) | |
| - String-form tool arguments parsed as JSON (from allanchan339) | |
| - Non-ASCII escaped in tools/args JSON (from froggeric) |
Chenx221
/ vs_26_22_19_17_dl.md
Last active
June 6, 2026 17:06
Visual Studio 2026/2022/2019/2017 Web Installer Direct download link
DvilMuck
/ aternosAntiAntiadblock.user.js
Last active
June 5, 2026 16:33
Aternos Anti Anti Adblock userscript for Aternos.org
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| // ==UserScript== | |
| // @name Aternos Anti Anti-adblock | |
| // @namespace r0630hh1edcuum5397kimyc0ucwy2h3psn4c6r1u4j | |
| // @version 0.1.341 | |
| // @description Fuck anti-adblock from the free hosting minecraft servers Aternos.org. Parry this you filthy casual! | |
| // @author Angry Developer against excessive ADs | |
| // @source https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a | |
| // @supportURL https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a | |
| // @updateURL https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a/raw/aternosAntiAntiadblock.user.js | |
| // @downloadURL https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a/raw/aternosAntiAntiadblock.user.js |
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| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no"> | |
| <title>Frontier Foundry — RTS</title> | |
| <link rel="preconnect" href="https://fonts.googleapis.com"> | |
| <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> | |
| <link href="https://fonts.googleapis.com/css2?family=Orbitron:wght@500;700;900&family=Rajdhani:wght@400;500;600;700&display=swap" rel="stylesheet"> | |
| <style> |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
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